Recently, text watermarking algorithms for large language models (LLMs) have
been mitigating the potential harms of text generated by the LLMs, including
fake news and copyright issues. However, the watermark detection of current
text algorithms requires the key from the generation process, making them
susceptible to breaches and counterfeiting. In this work, we propose the first
private watermarking algorithm, which extends the current text watermarking
algorithms by using two different neural networks respectively for watermark
generation and detection, rather than using the same key at both stages.
Meanwhile, part of the parameters of the watermark generation and detection
networks are shared, which makes the detection network achieve a high accuracy
very efficiently. Experiments show that our algorithm ensures high detection
accuracy with minimal impact on generation and detection speed, due to the
small parameter size of both networks. Additionally, our subsequent analysis
demonstrates the difficulty of reverting the watermark generation rules from
the detection network.

我们提出了第一种私有水印算法，通过使用两个不同的神经网络进行水印生成和检测，扩展了当前的文本水印算法，而不是在两个阶段都使用相同的密钥，实现了高效准确地检测网络，并且对生成和检测速度影响小。